
import itertools
import os
import os.path as osp
import pickle
import urllib
from collections import namedtuple

import numpy as np
# 稀疏矩阵
import scipy.sparse as sp
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import torch.optim as optim
import matplotlib.pyplot as plt

# ## 数据准备

# In[2]:

# 构建一个类似于class的 data命名元组
Data = namedtuple('Data', ['x', 'y', 'adjacency',
                           'train_mask', 'val_mask', 'test_mask'])

# 将数据转入到cuda中
def tensor_from_numpy(x, device):
    return torch.from_numpy(x).to(device)


class CoraData(object):
    filenames = ["ind.cora.{}".format(name) for name in
                 ['x', 'tx', 'allx', 'y', 'ty', 'ally', 'graph', 'test.index']]

    def __init__(self, data_root="./data/Cora/Cora/raw", rebuild=True):
        """Cora数据，包括数据下载，处理，加载等功能
        当数据的缓存文件存在时，将使用缓存文件，否则将下载、进行处理，并缓存到磁盘

        处理之后的数据可以通过属性 .data 获得，它将返回一个数据对象，包括如下几部分：
            * x: 节点的特征，维度为 2708 * 1433，类型为 np.ndarray
            * y: 节点的标签，总共包括7个类别，类型为 np.ndarray
            * adjacency: 邻接矩阵，维度为 2708 * 2708，类型为 scipy.sparse.coo.coo_matrix
            * train_mask: 训练集掩码向量，维度为 2708，当节点属于训练集时，相应位置为True，否则False
            * val_mask: 验证集掩码向量，维度为 2708，当节点属于验证集时，相应位置为True，否则False
            * test_mask: 测试集掩码向量，维度为 2708，当节点属于测试集时，相应位置为True，否则False

        Args:
        -------
            data_root: string, optional
                存放数据的目录，原始数据路径: ../data/cora
                缓存数据路径: {data_root}/ch5_cached.pkl
            rebuild: boolean, optional
                是否需要重新构建数据集，当设为True时，如果存在缓存数据也会重建数据

        """
        self.data_root = data_root
        save_file = osp.join(self.data_root, "ch5_cached.pkl")
        if osp.exists(save_file) and not rebuild:
            print("Using Cached file: {}".format(save_file))
            self._data = pickle.load(open(save_file, "rb"))
        else:
            self._data = self.process_data()
            with open(save_file, "wb") as f:
                # pickle提供了一个简单的持久化功能。可以将对象以文件的形式存放在磁盘上
                # 序列化对象，并将结果数据流写入到文件对象中
                pickle.dump(self.data, f)
            print("Cached file: {}".format(save_file))

    @property
    def data(self):
        """返回Data数据对象，包括x, y, adjacency, train_mask, val_mask, test_mask"""
        return self._data

    def process_data(self):
        """
        处理数据，得到节点特征和标签，邻接矩阵，训练集、验证集以及测试集
        引用自：https://github.com/rusty1s/pytorch_geometric
        """
        print("Process data ...")
        _, tx, allx, y, ty, ally, graph, test_index = [self.read_data(
            osp.join(self.data_root, name)) for name in self.filenames]
        print("tx:",tx)
        print("graph:",graph)
        print("allx:{}".format(allx))
        train_index = np.arange(y.shape[0])
        val_index = np.arange(y.shape[0], y.shape[0] + 500)
        sorted_test_index = sorted(test_index)

        # 沿现有轴连接一系列数组 0代表水平方向 1代表纵轴方向
        x = np.concatenate((allx, tx), axis=0)
        print('allx:',allx.shape,' tx:',tx.shape)
        print(x.shape)
        y = np.concatenate((ally, ty), axis=0).argmax(axis=1)
        print('ally:', allx.shape, ' ty:', tx.shape)
        print(y.shape)

        x[test_index] = x[sorted_test_index]
        y[test_index] = y[sorted_test_index]
        num_nodes = x.shape[0]

        train_mask = np.zeros(num_nodes, dtype=np.bool)
        val_mask = np.zeros(num_nodes, dtype=np.bool)
        test_mask = np.zeros(num_nodes, dtype=np.bool)
        train_mask[train_index] = True
        val_mask[val_index] = True
        test_mask[test_index] = True
        adjacency = self.build_adjacency(graph)
        print("Node's feature shape: ", x.shape)
        print("Node's label shape: ", y.shape)
        print("Adjacency's shape: ", adjacency.shape)
        print("Number of training nodes: ", train_mask.sum())
        print("Number of validation nodes: ", val_mask.sum())
        print("Number of test nodes: ", test_mask.sum())

        return Data(x=x, y=y, adjacency=adjacency,
                    train_mask=train_mask, val_mask=val_mask, test_mask=test_mask)

    @staticmethod
    def build_adjacency(adj_dict):
        """根据邻接表创建邻接矩阵"""
        edge_index = []
        num_nodes = len(adj_dict)
        for src, dst in adj_dict.items():
            edge_index.extend([src, v] for v in dst)
            edge_index.extend([v, src] for v in dst)
        # 去除重复的边
        edge_index = list(k for k, _ in itertools.groupby(sorted(edge_index)))
        edge_index = np.asarray(edge_index)
        adjacency = sp.coo_matrix((np.ones(len(edge_index)),
                                   (edge_index[:, 0], edge_index[:, 1])),
                                  shape=(num_nodes, num_nodes), dtype="float32")
        return adjacency

    @staticmethod
    def read_data(path):
        """使用不同的方式读取原始数据以进一步处理"""

        # 得到函数名
        name = osp.basename(path)
        if name == "ind.cora.test.index":
            out = np.genfromtxt(path, dtype="int64")
            return out
        else:
            # 反序列化对象 将文件中的数据解析为一个Python对象
            out = pickle.load(open(path, "rb"), encoding="latin1")
            out = out.toarray() if hasattr(out, "toarray") else out
            return out

    @staticmethod
    def normalization(adjacency):
        """计算 L=D^-0.5 * (A+I) * D^-0.5"""
        adjacency += sp.eye(adjacency.shape[0])  # 增加自连接
        # 计算度，因为是无向图邻接矩阵，所以沿水平或垂直方向都可以
        degree = np.array(adjacency.sum(1))
        # 根据每个节点的度，构建度函数
        d_hat = sp.diags(np.power(degree, -0.5).flatten())
        # 进行矩阵乘法
        return d_hat.dot(adjacency).dot(d_hat).tocoo()


# ## 图卷积层定义

# In[3]:


class GraphConvolution(nn.Module):
    def __init__(self, input_dim, output_dim, use_bias=True):
        """图卷积：L*X*\theta

        Args:
        ----------
            input_dim: int
                节点输入特征的维度
            output_dim: int
                输出特征维度
            use_bias : bool, optional
                是否使用偏置
        """
        super(GraphConvolution, self).__init__()
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.use_bias = use_bias
        self.weight = nn.Parameter(torch.Tensor(input_dim, output_dim))
        if self.use_bias:
            self.bias = nn.Parameter(torch.Tensor(output_dim))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        init.kaiming_uniform_(self.weight)
        if self.use_bias:
            init.zeros_(self.bias)

    def forward(self, adjacency, input_feature):
        """邻接矩阵是稀疏矩阵，因此在计算时使用稀疏矩阵乘法

        Args: 
        -------
            adjacency: torch.sparse.FloatTensor
                邻接矩阵
            input_feature: torch.Tensor
                输入特征
        """
        # mm 为矩阵乘法
        support = torch.mm(input_feature, self.weight)
        output = torch.sparse.mm(adjacency, support)
        if self.use_bias:
            output += self.bias
        return output

    def __repr__(self):
        return self.__class__.__name__ + ' (' + str(self.input_dim) + ' -> ' + str(self.output_dim) + ')'


# ## 模型定义
# 
# 读者可以自己对GCN模型结构进行修改和实验

# In[4]:


class GcnNet(nn.Module):
    """
    定义一个包含两层GraphConvolution的模型
    """

    def __init__(self, input_dim=1433):
        super(GcnNet, self).__init__()
        self.gcn1 = GraphConvolution(input_dim, 16)
        self.gcn2 = GraphConvolution(16, 7)

    def forward(self, adjacency, feature):
        h = F.relu(self.gcn1(adjacency, feature))
        logits = self.gcn2(adjacency, h)
        return logits


# ## 模型训练

# In[5]:


# 超参数定义
LEARNING_RATE = 0.1
WEIGHT_DACAY = 5e-4
EPOCHS = 200
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"

# In[7]:


# 加载数据，并转换为torch.Tensor
dataset = CoraData().data
node_feature = dataset.x / dataset.x.sum(1, keepdims=True)  # 归一化数据，使得每一行和为1
tensor_x = tensor_from_numpy(node_feature, DEVICE)
tensor_y = tensor_from_numpy(dataset.y, DEVICE)
tensor_train_mask = tensor_from_numpy(dataset.train_mask, DEVICE)
tensor_val_mask = tensor_from_numpy(dataset.val_mask, DEVICE)
tensor_test_mask = tensor_from_numpy(dataset.test_mask, DEVICE)
normalize_adjacency = CoraData.normalization(dataset.adjacency)  # 规范化邻接矩阵

num_nodes, input_dim = node_feature.shape
indices = torch.from_numpy(np.asarray([normalize_adjacency.row,
                                       normalize_adjacency.col]).astype('int64')).long()
values = torch.from_numpy(normalize_adjacency.data.astype(np.float32))
tensor_adjacency = torch.sparse.FloatTensor(indices, values,
                                            (num_nodes, num_nodes)).to(DEVICE)

# In[ ]:


# 模型定义：Model, Loss, Optimizer
model = GcnNet(input_dim).to(DEVICE)
criterion = nn.CrossEntropyLoss().to(DEVICE)
optimizer = optim.Adam(model.parameters(),
                       lr=LEARNING_RATE,
                       weight_decay=WEIGHT_DACAY)


# In[8]:


# 训练主体函数
def train():
    loss_history = []
    val_acc_history = []
    model.train()
    train_y = tensor_y[tensor_train_mask]
    for epoch in range(EPOCHS):
        logits = model(tensor_adjacency, tensor_x)  # 前向传播
        train_mask_logits = logits[tensor_train_mask]  # 只选择训练节点进行监督
        loss = criterion(train_mask_logits, train_y)  # 计算损失值
        optimizer.zero_grad()
        loss.backward()  # 反向传播计算参数的梯度
        optimizer.step()  # 使用优化方法进行梯度更新
        train_acc, _, _ = test(tensor_train_mask)  # 计算当前模型训练集上的准确率
        val_acc, _, _ = test(tensor_val_mask)  # 计算当前模型在验证集上的准确率
        # 记录训练过程中损失值和准确率的变化，用于画图
        loss_history.append(loss.item())
        val_acc_history.append(val_acc.item())
        print("Epoch {:03d}: Loss {:.4f}, TrainAcc {:.4}, ValAcc {:.4f}".format(
            epoch, loss.item(), train_acc.item(), val_acc.item()))

    return loss_history, val_acc_history


# In[9]:


# 测试函数
def test(mask):
    model.eval()
    with torch.no_grad():
        logits = model(tensor_adjacency, tensor_x)
        test_mask_logits = logits[mask]
        predict_y = test_mask_logits.max(1)[1]
        accuarcy = torch.eq(predict_y, tensor_y[mask]).float().mean()
    return accuarcy, test_mask_logits.cpu().numpy(), tensor_y[mask].cpu().numpy()


# In[13]:


def plot_loss_with_acc(loss_history, val_acc_history):
    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    ax1.plot(range(len(loss_history)), loss_history,
             c=np.array([255, 71, 90]) / 255.)
    plt.ylabel('Loss')

    ax2 = fig.add_subplot(111, sharex=ax1, frameon=False)
    ax2.plot(range(len(val_acc_history)), val_acc_history,
             c=np.array([79, 179, 255]) / 255.)
    ax2.yaxis.tick_right()
    ax2.yaxis.set_label_position("right")
    plt.ylabel('ValAcc')

    plt.xlabel('Epoch')
    plt.title('Training Loss & Validation Accuracy')
    plt.show()


# In[ ]:


loss, val_acc = train()
test_acc, test_logits, test_label = test(tensor_test_mask)
print("Test accuarcy: ", test_acc.item())

# In[14]:


plot_loss_with_acc(loss, val_acc)

# In[ ]:


# 绘制测试数据的TSNE降维图
from sklearn.manifold import TSNE

tsne = TSNE()
out = tsne.fit_transform(test_logits)
fig = plt.figure()
for i in range(7):
    indices = test_label == i
    x, y = out[indices].T
    plt.scatter(x, y, label=str(i))
plt.legend()

